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首页> 外文期刊>International Journal of Distributed Sensor Networks >Tracking maneuver target using interacting multiple model-square root cubature Kalman filter based on range rate measurement
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Tracking maneuver target using interacting multiple model-square root cubature Kalman filter based on range rate measurement

机译：基于距离率测量的交互多模型平方根库尔曼卡尔曼滤波器跟踪机动目标

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摘要

The problem of maneuvering target tracking is a hot issue in the field of target tracking. Due to the range rate measurement containing the maneuvering information of target, it has the important practical significance to study how to use the range rate measurement to improve the effect of maneuvering target tracking. In the framework of interacting multiple model algorithm, the range rate measurement is used to update target state estimate and the probability of motion model to improve the tracking performance. As the measurement equation including the range rate measurement is strongly nonlinear, square root cubature Kalman filter algorithm is selected as the filter in interacting multiple model algorithm. The normal acceleration is deduced from the range rate with the reality constraint. And through Monte Carlo simulation, the empirical distribution functions of the normal acceleration statistics corresponding to different motion models are obtained. Their approximate distribution functions are obtained by the use of the expectation maximization algorithm with Gaussian mixture model. Then the probability distribution and probability distribution of measurement prediction residual are combined into a new likelihood function to improve the efficiency of updating the model probability. The experimental results show that the interacting multiple model algorithm proposed in this article has the smaller root mean square error of position and velocity and has the smaller average Kullback-Leibler divergence of model probability during the motion model stable phase.
机译：机动目标跟踪问题是目标跟踪领域的一个热门问题。由于测距率测量中包含目标的机动信息，因此研究如何利用测距率测量来提高机动目标的跟踪效果具有重要的现实意义。在交互多模型算法的框架中，测距率测量用于更新目标状态估计和运动模型的概率，以提高跟踪性能。由于包括距离率测量在内的测量方程是强非线性的，因此在交互多模型算法中选择平方根库曼卡尔曼滤波算法作为滤波。从具有实际约束的测距率中得出法向加速度。并通过蒙特卡洛模拟，得到了对应于不同运动模型的正态加速度统计量的经验分布函数。通过使用期望最大化算法和高斯混合模型获得它们的近似分布函数。然后将测量预测残差的概率分布和概率分布组合成新的似然函数，以提高模型概率的更新效率。实验结果表明，本文提出的交互多模型算法在运动模型稳定阶段的位置和速度均方根误差较小，并且模型概率的平均Kullback-Leibler散度较小。

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